Bridging Perception and Action: Spatially-Grounded Mid-Level Representations for Robot Generalization (2506.06196v1)

Abstract: In this work, we investigate how spatially grounded auxiliary representations can provide both broad, high-level grounding as well as direct, actionable information to improve policy learning performance and generalization for dexterous tasks. We study these mid-level representations across three critical dimensions: object-centricity, pose-awareness, and depth-awareness. We use these interpretable mid-level representations to train specialist encoders via supervised learning, then feed them as inputs to a diffusion policy to solve dexterous bimanual manipulation tasks in the real world. We propose a novel mixture-of-experts policy architecture that combines multiple specialized expert models, each trained on a distinct mid-level representation, to improve policy generalization. This method achieves an average success rate that is 11% higher than a language-grounded baseline and 24 percent higher than a standard diffusion policy baseline on our evaluation tasks. Furthermore, we find that leveraging mid-level representations as supervision signals for policy actions within a weighted imitation learning algorithm improves the precision with which the policy follows these representations, yielding an additional performance increase of 10%. Our findings highlight the importance of grounding robot policies not only with broad perceptual tasks but also with more granular, actionable representations. For further information and videos, please visit https://mid-level-moe.github.io.

Spatially-Grounded Mid-Level Representations for Robot Generalization: An Analytical Overview

The paper "Bridging Perception and Action: Spatially-Grounded Mid-Level Representations for Robot Generalization" by Jonathan Yang et al. presents a novel approach to enhancing robot policy learning performance and generalization in dexterous manipulation tasks. The authors focus on incorporating spatially-grounded auxiliary representations that provide both high-level grounding and actionable information, addressing shortcomings in existing robotic models that struggle when faced with scene variations.

Key Contributions and Numerical Findings

The paper introduces a mixture-of-experts (MoE) policy architecture, leveraging multiple specialized expert models trained on distinct mid-level spatial representations. These representations are systematically categorized along three axes: object-centricity, pose-awareness, and depth-awareness. This methodology yields notable improvements, demonstrating an average of 11% higher success rates over language-grounded baselines and 24% higher success rates over standard diffusion policy baselines in dexterous bimanual tasks. Additionally, a weighted imitation learning algorithm, which uses mid-level representations as supervision signals, further enhances precision, resulting in a 10% increase in policy performance.

Implications and Future Directions

The exploration of spatially-grounded mid-level representations signifies an important step forward in robot policy generalization, highlighting the need for a balance between abstractions and actionable details. The implications of this research are multifaceted, promising enhancements in both practical manipulations and theoretical frameworks. Practically, the refined robot policies based on mid-level spatial representations offer improved adaptability across various tasks, incorporating both high-level and granular details needed for dexterous manipulation. This can be particularly beneficial in automation industries, household robotics, and scenarios requiring intricate dexterous tasks.

Theoretically, the paper of spatial grounding supports developing models that better infer physical relationships and geometric contexts. This opens up avenues for further exploration into dynamically adaptable models and architectures that can integrate seamlessly across varied environments. Looking ahead, future work might concentrate on advancing asynchronous representations, improving inference speed, and developing automated systems for collecting demonstration data. Additionally, advancements could focus on creating more generalized models capable of learning spatial relationships without extensive manual tuning. This would be essential for deploying robots in real-world contexts where learning agility and adaptability are critical.

Conclusion

In conclusion, Jonathan Yang and colleagues offer a robust strategy for refining robot generalization through spatially-grounded mid-level representations. The research achieves significant numerical improvements over prior baselines, indicating compelling applications in real-world robotic tasks. By comprehensively studying how different types of spatial representations can bolster robot dexterity, the paper paves the way for ongoing developments in robotic perception and action, aiming well beyond current limitations of scene adaptability.